Performance Metrics

Clustering Performance Analysis

Comprehensive performance evaluation of clustering algorithms including validation metrics, computational efficiency, and business impact analysis.

K-Means

Overall Performance Score

Silhouette Score0.724

Execution Time0.045s

Business Impact88%

Clusters: 3Accuracy: 85.2%

Hierarchical

B+

Overall Performance Score

Silhouette Score0.681

Execution Time0.123s

Business Impact78%

Clusters: 3Accuracy: 78.4%

DBSCAN

Overall Performance Score

Silhouette Score0.582

Execution Time0.087s

Business Impact65%

Clusters: 4Accuracy: 65.8%

Gaussian Mixture

B+

Overall Performance Score

Silhouette Score0.693

Execution Time0.156s

Business Impact82%

Clusters: 3Accuracy: 80.1%

Validation Metrics Comparison

Key clustering validation scores across algorithms

Metric Rankings

Algorithm rankings by validation metrics

Silhouette Score

1K-Means

0.724

2Gaussian Mixture

0.693

3Hierarchical

0.681

4DBSCAN

0.582

Execution Speed

1K-Means

0.045s

2DBSCAN

0.087s

3Hierarchical

0.123s

4Gaussian Mixture

0.156s

Business Impact

1K-Means

88%

2Gaussian Mixture

82%

3Hierarchical

78%

4DBSCAN

65%

Comprehensive Validation Matrix

Detailed comparison of all validation metrics

Algorithm	Silhouette	Calinski-H	Davies-B	Stability	Precision	Recall	F1-Score	Grade
K-Means	0.724	156.8	0.89	0.92	0.83	0.87	0.85	A
Hierarchical	0.681	142.1	0.95	0.88	0.76	0.81	0.78	B+
DBSCAN	0.582	98.4	1.23	0.75	0.68	0.72	0.70	C
Gaussian Mixture	0.693	148.3	0.92	0.84	0.79	0.83	0.81	B+